Title

Introduction

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Test reference to figure 1.

Test reference to table 1.

Mathjax

A little math formula :

\[x = \sum_{i=1}^{n} \sqrt{\frac{y}{i}} \]

Analysis

Tables

Here is a sample table output.

tab <- iris %>%
  group_by(Species) %>%
  summarise_all(.funs = mean)
kable(tab, caption = "Species-specific mean lengths and widths.")

Table 1: Species-specific mean lengths and widths.

Species	Sepal.Length	Sepal.Width	Petal.Length	Petal.Width
setosa	5.006	3.428	1.462	0.246
versicolor	5.936	2.770	4.260	1.326
virginica	6.588	2.974	5.552	2.026

Here is a crosstab displayed in several different ways with a “pills” interface. To do this, just pass your cross table result made with table() to the pilltabs() function.

iris$Sepal.Length <- factor(round(iris$Sepal.Length))
tab <- table(iris$Species, iris$Sepal.Length)
UHHformats::pilltabs(tab)

• Count
• Rows %
• Columns %
• Residuals

	4	5	6	7	8
setosa	5	40	5	0	0
versicolor	0	6	36	8	0
virginica	0	1	27	16	6

	4	5	6	7	8	Total	n
setosa	10.0	80.0	10.0	0	0	100	50
versicolor	0.0	12.0	72.0	16	0	100	50
virginica	0.0	2.0	54.0	32	12	100	50
All	3.3	31.3	45.3	16	4	100	150

	4	5	6	7	8	All
setosa	100	85.1	7.4	0.0	0	33.3
versicolor	0	12.8	52.9	33.3	0	33.3
virginica	0	2.1	39.7	66.7	100	33.3
Total	100	100.0	100.0	100.0	100	100.0
n	5	47.0	68.0	24.0	6	150.0

	4	5	6	7	8
setosa	2.58	6.15	-3.71	-2.83	-1.41
versicolor	-1.29	-2.44	2.80	0.00	-1.41
virginica	-1.29	-3.71	0.91	2.83	2.83

X-squared = 117.9305, df = 8, p = < 2.2e-16

Figures: ggplot2

An example histogram made with ggplot2:

d <- data.frame(x=rnorm(100))
ggplot(data=d) + geom_histogram(aes(x=x), bins = 30)

Figure 1: A ggplot2 histogram

Tabbed sections

Comparison between the classical tabs and the pill tabs:

Classical tabs

Classical tabs without fading effect.

First tab

hist(rnorm(100))

Figure 2: Histogram made with the basic graphics package

Second tab

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Pill tabs

Pill tabs with fading effect.

First tab

hist(rnorm(100))

Figure 3: Second histogram made with the basic graphics package

Second tab

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Plotly

mtcars %>%
  plot_ly(x = ~mpg, y = ~wt, color = ~cyl) %>%
  add_markers()

Figure 4: This is the interactive htmlwidget ‘plotly’

Conclusions

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